The mPower study, Parkinson disease mobile data collected using ResearchKit

By B. Bot, Christine Suver, E. C. Neto, M. Kellen, Arno Klein, J. Christopher Bare, M. Doerr, Abhishek Pratap, J. Wilbanks, E. R. Dorsey and 2 more. Scientific Data, 2016

600 citations recorded by the source corpus.

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health. Design Type(s) observation design • time series design • repeated measure design Measurement Type(s) disease severity measurement Technology Type(s) Patient Self-Report Factor Type(s) Sample Characteristic(s) Homo sapiens Design Type(s) observation design • time series design • repeated measure design Measurement Type(s) disease severity measurement Technology Type(s) Patient Self-Report Factor Type(s) Sample Characteristic(s) Homo sapiens Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/12112e6da81972341eb63919db682eb548b36929

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